A population-specific reference panel for improved genotype imputation in African Americans

There is currently a dearth of accessible whole genome sequencing (WGS) data for individuals residing in the Americas with Sub-Saharan African ancestry. We generated whole genome sequencing data at intermediate (15×) coverage for 2,294 individuals with large amounts of Sub-Saharan African ancestry, predominantly Atlantic African admixed with varying amounts of European and American ancestry. We performed extensive comparisons of variant callers, phasing algorithms, and variant filtration on these data to construct a high quality imputation panel containing data from 2,269 unrelated individuals. With the exception of the TOPMed imputation server (which notably cannot be downloaded), our panel substantially outperformed other available panels when imputing African American individuals. The raw sequencing data, variant calls and imputation panel for this cohort are all freely available via dbGaP and should prove an invaluable resource for further study of admixed African genetics.

Abstract LB-110: Computational discovery and preclinical validation of therapeutic leads with novel MOAs for hepatocellular carcinoma and pancreatic ductal adenocarcinoma

Hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma (PDAC) have among the lowest 5-year survival rates of all cancer types at 18% and 9%, respectively. Treatment options for patients with liver or pancreatic cancer are relatively unchanged over the past 10 years. HCC has seen the recent FDA-approval of multi-kinase inhibitor therapies with similar mechanisms of action, including cabozantinib, regorafenib, and lenvatinib, and the immune checkpoint inhibitor nivolumab (conditionally). Despite these advances, the survival rate and median survival time for HCC patients remain poor. The picture for PDAC patients is similar, although with even greater need for new therapies. We present results from a powerful and efficient computational drug discovery platform that produces drug discovery hits with first-in-class mechanisms of action that can advance rapidly and successfully through preclinical validation studies. The twoXAR discovery platform uses an artificial-intelligence framework to integrate diverse patient-derived data sets and build holistic and unbiased models of human disease biology. The utilization of diverse, proprietary algorithms and deep learning principles provides a highly sensitive platform to elucidate detailed disease-specific associations between biology and biomedical data that are integrated with a library of existing drug molecules to deliver novel, high-value drug discovery hits. The twoXAR platform delivers drug discovery hits with known pharmacological properties and preserves the data-driven links to disease biology; this facilitates validation and optimization studies. We employed the twoXAR platform to build in-silico disease models of HCC and PDAC using disease-specific data and generated a set of 10 molecules with predicted efficacy in HCC and a second, independent set of 11 molecules with predicted efficacy in PDAC. These independent sets of disease-specific drug discovery hits represented novel mechanisms of action that had not been tested previously as potential clinical therapies for HCC or PDAC, respectively. TXR-311 and TXR-312, and TXR-405 and TXR-411 were discovered as validated hits for HCC and PDAC, respectively, using in vitro cell proliferation and viability assays with HCC and PDAC tumor cell lines. In these studies, TXR-311 inhibited proliferation and viability of five different HCC tumor cell lines with IC50 values that were 70-fold lower than IC50 values for sorafenib and displayed greater than 500-fold selectivity against primary human hepatocytes. In subsequent in vivo efficacy studies using two HCC patient-derived xenograft (PDX) tumor models, TXR-311 showed excellent tolerability and displayed significant tumor growth inhibition efficacy compared to vehicle-treated controls. TXR-311 presents a first-in-class lead for further development as a potential HCC therapy.

Citation Format: Isaac Hakim, Mei-Sze Chua, Wei Wei, Li Ma, Elizabeth Noblin, Samuel So, Aaron C. Daugherty, Timothy S. Heuer. Computational discovery and preclinical validation of therapeutic leads with novel MOAs for hepatocellular carcinoma and pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-110.